Surface water simulation with the PyCatch model and verification with remote sensing data for malaria prediction

Abstract

Malaria affects more than 200 million people in the world each year. It is increasingly restricted to tropical and subtropical areas, especially in Africa, resulting in lower dependence on air temperature, so more attention is paid to modeling malaria with surface water processes in recent years. The PyCatch model enables dynamically simulating the processes of interception, evapotranspiration, surface storage, infiltration, subsurface flow, and overland flow. To examine the PyCatch capability in simulating malaria-relevant surface water processes, we applied the PyCatch model to a small catchment and evaluated the model performance in discharge and soil moisture simulation. The results show the consistency between PyCatch hydrograph and other global hydrological models, and the PyCatch skillfulness in discharge simulation. The soil moisture simulated by PyCatch is stable and has evident seasonal and interannual variation patterns, despite some differences against the remote sensing data. Through linear regression, PyCatch is proved to be competent for malaria burden prediction.